Examining Recommendations for Artificial Intelligence Use with Integrity from a Scholarship of Teaching and Learning Lens

Abstract

New developments in the Artificial Intelligence (AI) field allowed the development of Generative Artificial Intelligence (GenAI), capable of creating text resembling what humans can produce. As a result, educators’ concerns in the higher education sector quickly emerged. Many organizations and experts have addressed these concerns through recommendations. In this conceptual paper, we draw from the Integrated Model for Academic Integrity through a Scholarship of Teaching and Learning Lens to examine and stimulate discussion from eleven documents that focus on using GenAI with integrity. We identified recommendations suitable for the individual (micro), the departmental/program (meso), the institutional (macro), and the interinstitutional/ national/ international (mega) levels concerning two core elements of the model: “high-impact professional learning for individuals and groups” and “local-level leadership and microcultures.” Suggestions around the core element “scholarship, research and inquiry” were lacking at the micro and meso levels; likewise, recommendations for the core element “learning spaces, pedagogies, and technologies” were also absent at the meso, macro, and mega levels. We acknowledge that these recommendations focus on learning, involve various stakeholders, and go beyond student conduct, which aligns with current approaches to academic integrity. However, some gaps need further exploration. We highlight the need to develop more specific and practical guidance and resources for educational stakeholders around GenAI issues related to academic integrity, explore how to better support networks and leaders in higher education in creating the conditions for ethical GenAI use, and emphasizing the need for an Equity, Diversity, and Inclusion lens on GenAI.